Multilayer feedforward networks are universal approximators
Neural Networks
Forecasting tourism demand based on empirical mode decomposition and neural network
Knowledge-Based Systems
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In this paper a methodology for rainfall forecasting is presented, using the principle of decomposition and ensemble. In the proposed framework, the employed decomposition technique is the Ensemble Empirical Mode Decomposition (EEMD), which divides the original data into a set of simple components. Each component is modeled with a Feed Forward Neural Network (FNN) as a forecasting tool. Finally, the individual forecasting results for all components are combined to obtain the prediction result of the input signal. Experiments were performed on a real-observed rainfall data, and the attained results were compared against a single FNN model for the raw data, showing an improvement on the system performance.